From the ready room to the battle bus: exploring militarisation through gamespace soundwalks in Fortnite
Bibliographic record
Abstract
While its candy-coated shell may provide a clever camouflage for younger markets, this paper acknowledges that militarisation has been made pleasurable in more sensuous ways. Deploying gamespace soundwalking as a method, we attune to the ways Fortnite's sound design rehearses the neoliberal citizen-soldier on a sonic dimension. Yet, its capitalist priorities both corner youth markets while letting young players experiment with counterplay. To arrive at this conclusion, this paper illustrates the application of a novel method within game and sound studies – gamespace soundwalking. In the case of Fornite where the narrative context is individual military victory, the affective atmosphere ought to be one of keen attention to warfare, ambience and combat events. But how is war specifically depicted there? What is the significance of the sonic environment in forming a sense of place? We argue that method must take us beyond a semiotic analysis of the sonic components of Fortnite; we need a real-time ethnographic exploration of gameplay. In so doing, we tune in to Fortnite's ear-candy sound design as it exemplifies neoliberal standardisation of sound environments – masking grizzly conflict zones – as well as consumptive priorities that reach spectacular levels and the militarised Taylorist perfection of the citizen-soldier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".